Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Qwen3.5-397B-A17B supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Moonshot AI
Kimi K2.5, released by Moonshot AI in January 2026, is an updated Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters. It builds on Kimi K2 with improved coding performance across multiple languages and an expanded context window. Kimi K2.5 targets agentic development workflows, polyglot code generation, and open-source deployments requiring large-scale MoE reasoning.
Alibaba / Qwen
Qwen3.5-397B-A17B is a 397-billion-parameter mixture-of-experts model from Alibaba's Qwen team, released in February 2026 as the open-weight flagship of the Qwen3.5 series, featuring 17 billion active parameters per forward pass through a hybrid linear-attention and sparse-MoE architecture based on Gated Delta Networks. The model was co-trained on text, images, and video using early fusion, making it natively multimodal across a 262K token context window, while achieving significantly higher inference throughput than comparable dense models due to its sparse computation design. At release it was one of the most capable open-weight models publicly available, offered under Apache 2.0 and accessible through Alibaba's DashScope API as the Qwen3.5-Plus endpoint.
1 month newer
Kimi K2.5
Moonshot AI
2026-01
Qwen3.5-397B-A17B
Alibaba / Qwen
2026-02-16
Context window and performance specifications
Average performance across 1 common benchmarks
Kimi K2.5
Qwen3.5-397B-A17B
Performance comparison across key benchmark categories
Kimi K2.5
Qwen3.5-397B-A17B
Available providers and their performance metrics
Kimi K2.5
Moonshot AI
Qwen3.5-397B-A17B
Kimi K2.5
Qwen3.5-397B-A17B
Kimi K2.5
Qwen3.5-397B-A17B